{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78361d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 男女绘图\n",
    "import matplotlib\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# 中文乱码和坐标轴负号处理。\n",
    "matplotlib.rc('font', family='SimHei', weight='bold')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "sex = ['Male', 'Female']\n",
    "colors = ['red', 'yellow']\n",
    "data = [128, 445]\n",
    "\n",
    "# 为横条设置标签\n",
    "fig,ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 2))\n",
    "b = ax.barh(range(len(sex)), data, tick_label=sex, color=colors)\n",
    "\n",
    "#为横向水平的柱图右侧添加数据标签。\n",
    "for rect in b:\n",
    "    w = rect.get_width()\n",
    "    plt.text(w, rect.get_y() + rect.get_height()/2, ' %d' %\n",
    "            int(w), ha='left', va='center', fontsize='15')\n",
    " \n",
    "#设置Y轴纵坐标上的刻度线标签。\n",
    "ax.set_yticks(range(len(sex)))\n",
    "ax.set_yticklabels(sex, fontsize=15, font=\"Monaco\")\n",
    " \n",
    "#不要X横坐标上的label标签。\n",
    "plt.xticks(())\n",
    "\n",
    "plt.title('水平横向的柱状图', loc='center', fontsize='25',\n",
    "          fontweight='bold', color='red')\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(12, 4))\n",
    "#ax1 = plt.subplots(2, 2, 2)\n",
    "colors = ['pink', 'green']\n",
    "plt.pie(data,\n",
    "      explode=(0,0),\n",
    "      labels=sex,\n",
    "      colors=colors,\n",
    "      autopct = '%3.2f%%', #数值保留固定小数位\n",
    "      shadow = False, #无阴影设置\n",
    "      startangle =90, #逆时针起始角度设置\n",
    "      pctdistance = 0.6) #数值距圆心半径倍数的距离\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a8a9811",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "y1 = np.cos(x)\n",
    "plt.figure(figsize=(18, 4))  # 整个现实图（框架）的大小\n",
    "plt.plot(x, y, 'r-o', label=\"$sin(x)$\", linewidth=1)\n",
    "plt.plot(x, y1, 'b-o', label=\"$cose(x)$\", linewidth=1)\n",
    "plt.xlabel('Time(s)')\n",
    "plt.ylabel(\"Volt\")\n",
    "plt.title(\"Python chart\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20921a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt   # Python画图工具\n",
    "from matplotlib.gridspec import GridSpec     # 利用网格确定图形的位置\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用于显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用于显示中文\n",
    "\n",
    "# plt.rcParams['font.sans-serif']=['Times New Roman']\n",
    "\n",
    "fig.suptitle('sex', fontsize=30, x=0.5, y=0.93)\n",
    "\n",
    "gs = GridSpec(40, 40)\n",
    "\n",
    "# 第一个子图\n",
    "# ax1是axes对象，这一步意思是ax1画的图在原图(40*40)占据行5-14，占据列5-14(从零开始索引)\n",
    "ax1 = fig.add_subplot(gs[5:10, 5:25])\n",
    "sex = ['Male', 'Female']\n",
    "colors = ['red', 'yellow']\n",
    "data = [128, 445]\n",
    "\n",
    "# 为横条设置标签\n",
    "b = ax1.barh(range(len(sex)), data, tick_label=sex, color=colors)\n",
    "for rect in b:\n",
    "    w = rect.get_width()\n",
    "    plt.text(w, rect.get_y() + rect.get_height()/2, '%d ' %\n",
    "             int(w), ha='left', va='center', fontsize='15')\n",
    "ax1.set_yticks(range(len(sex)))\n",
    "ax1.set_yticklabels(sex, fontsize=15, font=\"Monaco\")\n",
    "ax1.set_xticks(())\n",
    "ax1.set_title('水平横向的柱状图', loc='center', fontsize='20',\n",
    "              fontweight='bold', color='red', x=0.5, y=1.22)\n",
    "# 第二个子图\n",
    "# ax2画的图在原图(40*40)占据行20-39，占据列20-39(从零开始索引)\n",
    "ax2 = fig.add_subplot(gs[:12, 27:])\n",
    "colors = ['pink', 'green']\n",
    "ax2.set_title('饼图', fontsize=20, x=0.5, y=1.02)\n",
    "patches, l_text, p_text = ax2.pie(data,\n",
    "                                  explode=(0, 0.03),\n",
    "                                  labels=sex,\n",
    "                                  colors=colors,\n",
    "                                  autopct='%3.2f%%',  # 数值保留固定小数位\n",
    "                                  shadow=False,  # 无阴影设置\n",
    "                                  startangle=90,  # 逆时针起始角度设置\n",
    "                                  pctdistance=0.6)  # 数值距圆心半径倍数的距离\n",
    "\n",
    "# 改变饼图文本的大小\n",
    "# 方法是把每一个text遍历。调用set_size方法设置它的属性\n",
    "for t in l_text:\n",
    "    t.set_size(15)\n",
    "for t in p_text:\n",
    "    t.set_size(15)\n",
    "# 设置x，y轴刻度一致，这样饼图才能是圆的\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1da516bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a657f1dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbd8173f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
